Chronic cough has proven to be one of the more challenging therapeutic areas in respiratory medicine. Despite years of scientific progress, significant investment, and multiple late-stage clinical development programs, there are still no FDA-approved therapies available for refractory or unexplained chronic cough in the United States, a condition that affects more than 12 million people.¹

Against that backdrop, recent developments in chronic cough research have prompted reflection across the respiratory community. Rather than focusing on the outcome of any single study, it is worth stepping back to consider something broader: the remarkable progress that has been made in objectively monitoring cough and the opportunities that remain to further advance how cough is measured in clinical trials.

Objective measurement has transformed chronic cough research

Over the past decade, 24-hour ambulatory cough monitoring has fundamentally changed chronic cough clinical trials. More recently, objective cough monitoring has also been used in clinical trials for other respiratory indications, including idiopathic pulmonary fibrosis (IPF), interstitial lung disease (ILD), COPD, subacute cough, and infectious diseases.

Combining objective cough monitoring with symptom recall and questionnaires has provided researchers with reliable, reproducible measures of cough burden and has helped establish chronic cough as an important therapeutic area. Objective 24-hour cough frequency is now recognized as a primary endpoint in chronic cough trials and has played an important role in evaluating emerging therapies. International guidelines from both the European Respiratory Society (ERS) and CHEST recognize the importance of objective cough measurement in chronic cough research and evaluation.^2,3

Progress doesn’t stop with one measure

Cough severity is considered to include three domains:

• Frequency: How often a patient coughs
• Intensity: How forceful or loud the cough is
• Disruption: How much coughing interferes with daily life, including speaking, sleeping, social interaction, and quality of life⁴

Figure 1: Dimensions of cough severity, adapted from a presentation by Professor Jacky Smith, Measuring Cough, at the ERS Cough Conference 2026.

Today, frequency is the only dimension that is consistently being measured objectively in cough studies. Intensity and disruption are traditionally assessed using patient-reported outcome measures such as the Visual Analogue Scale (VAS) for cough severity and the Leicester Cough Questionnaire (LCQ) for cough-specific quality of life.

Importantly, emerging evidence suggests these dimensions are not interchangeable. In the PROCOUGH study, improvements in objective cough frequency did not always correspond with improvements in cough severity or cough-specific quality of life, highlighting that each measure captures a different aspect of the patient experience.⁵

This raises an exciting opportunity. Rather than asking whether objective cough frequency should be replaced, the question becomes: Can we develop objective counterparts to these additional dimensions of cough severity?

The goal is not to replace patient-reported outcomes or objective cough frequency, but to build upon and complement with additional objective metrics to improve how we characterize cough.

The next generation of objective cough measures

Advances in digital health technologies, wearable device technologies, and machine learning are making it increasingly feasible to objectively characterize cough in new ways.

Using cough monitoring technologies developed by Strados Labs and others in the field, researchers are actively exploring measures such as:

• Cough bouts
• Cough intensity
• Cough duration
• Relationships between cough and sleep, activity, speech, and other physiological signals

Recent evidence suggests that some of these novel measures may correlate more closely with patient-reported outcomes in some settings. A recent study in progressive pulmonary fibrosis found that objectively measured cough intensity correlated with patient-reported outcomes, highlighting the potential value of objective intensity as an additional dimension of cough burden worthy of further investigation.⁶

Another particularly exciting area of investigation is longitudinal cough monitoring.

While 24-hour cough monitoring periods have provided a foundation for modern cough clinical trials, advances in wearable technologies invite an important scientific question:

If we can now measure cough reliably over multiple consecutive days, should we?

Cough is inherently variable and can fluctuate for a variety of reasons, including environmental exposures, physical activity, sleep, respiratory infections, and medication adherence. A 24-hour period provides an important window, but it may not always capture the full picture.

Recent literature suggests that multi-day monitoring is increasingly feasible and scientifically appealing. Studies have demonstrated meaningful day-to-day variability in cough burden and high adherence to wearable respiratory monitoring over five to seven days across multiple respiratory diseases.⁷ Longer-duration studies have also demonstrated that extended monitoring can reveal clinically meaningful changes in cough characteristics over time and reinforce the dynamic nature of cough, supporting further investigation into whether monitoring beyond a single 24-hour period may provide additional insight in some study designs.⁶

As monitoring expands from one day to one week or longer, another challenge emerges: data review.

For traditional 24-hour cough monitoring, manual human counting has played an important role in ensuring the accuracy and quality of objective cough frequency measurements for primary endpoints. However, as monitoring durations increase, fully reviewing every hour of recorded data quickly manually becomes impractical from both a time and cost perspective.

At the other extreme, relying on automated cough detection via machine learning algorithms, while highly efficient, may limit opportunities for independent quality review, and the level of transparency often expected for clinical trial endpoints.

Fortunately, new hybrid approaches are beginning to emerge. For example, techniques such as error prediction models can identify recording segments that are most likely to contain algorithm errors (i.e. high background noise), enabling targeted human review where it is most valuable while allowing the majority of the data to be processed automatically.⁸ This approach has the potential to improve operational efficiency while preserving raw source data, auditability, and human oversight expected for high-quality clinical research.

The future is likely complementary, not competitive

The evolution of endpoint science has rarely involved replacing one measure with another. More often, it involves adding new tools that provide additional context and a more complete understanding of disease.

We believe the same to be true in chronic cough.

The next chapter may involve developing objective measures that complement both cough frequency and traditional patient-reported outcomes, capturing not only how often patients cough, but also how intensely they cough and how coughing affects their daily lives.

It may also involve moving beyond 24-hour periods toward longitudinal assessment, helping researchers better understand how cough changes over time and determine when multi-day monitoring can provide a more representative picture of disease burden and treatment response.

By continuing to refine these tools, learn from each study, and ask increasingly better questions, we are optimistic that the respiratory research community can continue advancing objective cough assessment in ways that best support patients, investigators, sponsors, and regulators.

References

1. Allergy & Asthma Network. (n.d.). Understanding refractory chronic cough. Retrieved July 24, 2026, from https://allergyasthmanetwork.org/health-a-z/chronic-cough/
2. Morice AH, Millqvist E, Bieksiene K, et al. ERS guidelines on the diagnosis and treatment of chronic cough in adults and children. European Respiratory Journal. 2020.
3. Gibson PG, Vertigan AE, et al. CHEST Guideline and Expert Panel Report on chronic cough and chronic cough assessment.
4. Birring SS, Prudon B, Carr AJ, Singh SJ, Morgan MDL, Pavord ID. Development of the Leicester Cough Questionnaire (LCQ). European Respiratory Journal. 2003.
5. Wahab, M., Birring, S. S., et al. (2026). Prospective study to evaluate the effectiveness of guideline-based therapies in chronic cough using patient-reported outcomes and objective cough monitoring (PROCOUGH). ERJ Open Research. Advance online publication. https://doi.org/10.1183/23120541.00279-2026
6. Feist, M. D., Huang, Y., Kalluri, M., Cole, J., Naikyar, E., Boulanger, P., Zanini, U., Birring, S. S., & Ferrara, G. (2026). Decoding objective cough features in progressive pulmonary fibrosis: A 6-month feasibility study. The American Journal of Medicine, 139(1), 99–107. https://doi.org/10.1016/j.amjmed.2025.07.027
7. Chung, K. F., Chaccour, C., Jover, L., Galvosas, M., Song, W.-J., Rudd, M., & Small, P. (2024). Longitudinal cough frequency monitoring in persistent coughers: Daily variability and predictability. Lung, 202(5), 561–568. https://doi.org/10.1007/s00408-024-00734-x
8. deLaubenfels T, Powers R, Kroh J, Marinovich A. Increased Accuracy of the CoughCheck Automated Cough Detection Algorithm in Real World Settings via Predictive Modeling of Error-Prone Segments. American Journal of Respiratory and Critical Care Medicine, Volume 212, Issue Supplement_1, May 2026, aamag162.2091, https://doi.org/10.1093/ajrccm/aamag162.2091

Author

Tom deLaubenfels, PhD
Senior Director, Data Science